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A neural network model for stroboscopic alternative motion

H O Carmesin1, S Arndt

  • 1Department of Theoretical Neurophysics, University of Bremen, Germany.

Biological Cybernetics
|September 1, 1996
PubMed
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This study presents a neural network modeling multistable perception using stochastic dynamics and resource mechanisms. The model accurately explains various perceptual phenomena, including oscillations, through continuous phase transitions.

Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Multistable perception involves experiencing multiple distinct percepts from a single, unchanging stimulus.
  • Existing models often struggle to capture the dynamic transitions and saturation effects observed in human perception.
  • Understanding the neural basis of perceptual switching is crucial for cognitive neuroscience.

Purpose of the Study:

  • To develop a novel neural network model that simulates multistable perception.
  • To investigate the role of stochastic dynamics, Hebbian coupling, and resource limitations in perceptual stability.
  • To explain empirical findings in multistable perception, including succession, alternative motion, simultaneity, and oscillation.

Main Methods:

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  • A neural network architecture comprising sensor and inner neurons was designed.
  • The network dynamics were defined using stochastic neuronal dynamics, Hebb-type coupling, and a resource saturation mechanism.
  • A system of coupled differential equations was derived and analyzed to understand network behavior.

    • Main Results:

    • The model successfully binds single stimuli to a unique percept, even during multistability.
    • The network exhibits both discontinuous and continuous phase transitions, mirroring empirical observations.
    • Specific perceptual phenomena like succession, alternative motion, simultaneity, and oscillation were modeled accurately.

    Conclusions:

    • The proposed neural network provides a robust framework for understanding multistable perception.
    • The model's ability to replicate diverse perceptual phenomena highlights the importance of its dynamic components.
    • Oscillatory percepts can be explained by continuous phase transitions within the neural network model.